SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1900 of 4925 papers

TitleStatusHype
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing—0
Autoregressive Speech Synthesis without Vector Quantization—0
Applying generative neural networks for fast simulations of the ALICE (CERN) experimentCode0
ERQ: Error Reduction for Post-Training Quantization of Vision Transformers—0
Ternary Spike-based Neuromorphic Signal Processing System—0
ZOBNN: Zero-Overhead Dependable Design of Binary Neural Networks with Deliberately Quantized Parameters—0
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM CompressionCode0
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT—0
Balance of Number of Embedding and their Dimensions in Vector Quantization—0
Quantizing YOLOv7: A Comprehensive Study—0
Hybrid Receiver Design for Massive MIMO-OFDM with Low-Resolution ADCs and Oversampling—0
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models—0
Resource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation MapsCode0
Low-latency machine learning FPGA accelerator for multi-qubit-state discrimination—0
Joint Beamforming Design and Bit Allocation in Massive MIMO with Resolution-Adaptive ADCs—0
Timestep-Aware Correction for Quantized Diffusion Models—0
QET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering—0
GPTQT: Quantize Large Language Models Twice to Push the Efficiency—0
Fisher-aware Quantization for DETR Detectors with Critical-category Objectives—0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers—0
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations—0
SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic—0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment—0
Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation—0
OSPC: Artificial VLM Features for Hateful Meme Detection—0
How Does Quantization Affect Multilingual LLMs?—0
Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural NetworksCode0
Exploring FPGA designs for MX and beyond—0
Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression—0
PQCache: Product Quantization-based KVCache for Long Context LLM Inference—0
Linear and Nonlinear MMSE Estimation in One-Bit Quantized Systems under a Gaussian Mixture Prior—0
NeuroNAS: Enhancing Efficiency of Neuromorphic In-Memory Computing for Intelligent Mobile Agents through Hardware-Aware Spiking Neural Architecture Search—0
Toward a Diffusion-Based Generalist for Dense Vision Tasks—0
Rateless Stochastic Coding for Delay-Constrained Semantic Communication—0
Deep Fusion Model for Brain Tumor Classification Using Fine-Grained Gradient Preservation—0
Reliable edge machine learning hardware for scientific applications—0
Fronthaul Quantization-Aware MU-MIMO Precoding for Sum Rate Maximization—0
Efficient course recommendations with T5-based ranking and summarizationCode0
MCNC: Manifold Constrained Network Compression—0
OutlierTune: Efficient Channel-Wise Quantization for Large Language Models—0
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization—0
A Quantization-based Technique for Privacy Preserving Distributed Learning—0
Differential error feedback for communication-efficient decentralized learning—0
CDQuant: Greedy Coordinate Descent for Accurate LLM Quantization—0
Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-LevelsCode0
Reducing the Memory Footprint of 3D Gaussian Splatting—0
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other—0
Approximate DCT and Quantization Techniques for Energy-Constrained Image Sensors—0
BitNet b1.58 Reloaded: State-of-the-art Performance Also on Smaller Networks—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified